A Novel Framework for Secure and Interoperable Digital Twins in the Industrial Metaverse

Adoption of the industrial metaverse in manufacturing is limited by architectural barriers in Digital Twins (DTs), notably centralised data collection, data-sovereignty conflicts, vendor silos, and weak decentralised governance, which increase privacy risks and hinder collaboration. This paper presents a four-layer framework that combines hierarchical federated learning for privacy-preserving edge intelligence, proof-of-authority blockchain, hybrid physics-informed and data-driven DT simulation, and human-in-the-loop metaverse interaction. Using the NASA C-MAPSS FD001 dataset, the framework achieved an RMSE of 12.49 for RUL prediction, outperformed FedAvg by 23%, remained within 2.8% of centralised training, and cut communication overhead by over 50%.